ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency

• URL: http://arxiv.org/abs/2302.10307v1
• Date: Tue, 31 Jan 2023 01:57:52 GMT
• Title: ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency
• Authors: Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, Xiaodan Liang
• Abstract summary: We propose multi-textbfView textbfConsistent learning (ViewCo) for text-supervised semantic segmentation. We first propose text-to-views consistency modeling to learn correspondence for multiple views of the same input image. We also propose cross-view segmentation consistency modeling to address the ambiguity issue of text supervision.
• Score: 126.88107868670767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existing works focus on pixel grouping and cross-modal semantic alignment, while ignoring the correspondence among multiple augmented views of the same image. To overcome such limitation, we propose multi-\textbf{View} \textbf{Co}nsistent learning (ViewCo) for text-supervised semantic segmentation. Specifically, we first propose text-to-views consistency modeling to learn correspondence for multiple views of the same input image. Additionally, we propose cross-view segmentation consistency modeling to address the ambiguity issue of text supervision by contrasting the segment features of Siamese visual encoders. The text-to-views consistency benefits the dense assignment of the visual features by encouraging different crops to align with the same text, while the cross-view segmentation consistency modeling provides additional self-supervision, overcoming the limitation of ambiguous text supervision for segmentation masks. Trained with large-scale image-text data, our model can directly segment objects of arbitrary categories in a zero-shot manner. Extensive experiments show that ViewCo outperforms state-of-the-art methods on average by up to 2.9\%, 1.6\%, and 2.4\% mIoU on PASCAL VOC2012, PASCAL Context, and COCO, respectively.

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